The Unassuming Economist

From Econofact post by Lant Pritchett:

The Issue:

Reducing poverty in developing countries has been a longstanding and central concern of development economics. Over the past two decades, there has been a noticeable shift in the approach taken by development economists to address this question. Researchers have increasingly focused their efforts on randomized controlled trials — an experimental approach commonly used in medical research — to determine the effectiveness of anti-poverty programs and interventions. This shift was highlighted by the 2019 Nobel Prize in Economics awarded to Abhijit Banerjee, Esther Duflo and Michael Kremer, which recognized their work for breaking down the issue of fighting global poverty into “smaller, more manageable, questions – for example, the most effective interventions for improving educational outcomes or child health.” One question that remains, though, is how much overall reduction in the poverty rate one can expect from projects, programs or policy interventions that raise the well-being of those in absolute poverty at a given level of income, versus how much poverty reduction comes from broad-based economic growth.

The Facts:

• In the last quarter century 1.1 billion people, about one-seventh of the world’s population, have been lifted out of extreme poverty. Yet progress has been uneven across different regions and significant challenges remain. A dramatic reduction of extreme poverty in East Asia, particularly in China, accounts for an important share of the advances in combating global poverty, with poverty reductions in South Asia also contributing their share. In contrast, progress has been much slower in Sub-Saharan Africa (see here). The World Bank counts all persons in a household as “poor” if the household per capita daily consumption or income is below a “poverty line.” It uses three different thresholds, $1.90, $3.20, and $5.50 per person per day, where local currency values are translated into 2011 dollar values, and taking into account the fact that goods and services are cheaper in poorer countries (so-called “purchasing power parity” currency conversion rates). The World Bank notes a marked reduction in extreme poverty (less than $1.90 per day) over the past quarter century, with a decrease from 36 percent in 1990 to 10 percent in 2015. Still, over 700 million people around the world continued to live in extreme poverty in 2015.

• One way to consider the relationship between economic growth and poverty is to look across countries and compare median incomes and the share of the population living in poverty. The median income is the income level at which half the population has an income higher than this amount and half the population has an income below this amount. Statisticians focus on median income rather than average income because a small number of people with very high levels of income alter the average much more than the median and can give rise to a distorted picture of the income profile of a country.

• In theory, two countries could have the same median income and yet have very different poverty rates. Differences in how income is distributed within countries could make it so that two countries with the same median income could have very different poverty rates. For an extreme example, consider a country with a median income of $5,000 per capita (the equivalent of about $13.70 per person per day); This country could have no one living with an income of less than $5.50 per day, effectively having a rate of zero poverty under a $5.50 dollar-a-day threshold; Alternatively, half of the population could be living with an income less than $5.50 a day (in which case no one would have an income in the range between $5.50 and $13.70) — and, in this case, there would be a 50 percent poverty rate. Thus, there is not necessarily a tight relationship between the median income in a country and its poverty rate.”

Continue reading here.

From an IMF article:

“South Africa suffers among the highest levels of inequality in the world when measured by the commonly used Gini index. Inequality manifests itself through a skewed income distribution, unequal access to opportunities, and regional disparities. Low growth and rising unemployment have contributed to the persistence of inequality.

The South African government has used different tools to tackle the stubborn levels of inequality that have plagued the country, including through progressive fiscal redistribution.

Efforts to reduce inequality have focused on higher social spending, targeted government transfers, and affirmative action to diversify wealth ownership and promote entrepreneurship among the previously marginalized. These measures need to be complemented with reforms that promote private investment, jobs, and inclusive growth.

Here are six charts that tell the story of South Africa’s inequality:

Inequality has remained stubbornly high. South Africa started the 1990s with already elevated inequality as the policy of apartheid excluded a large swath of the population from economic opportunities. South Africa’s Gini—an index that measures inequality—has increased further in the early 2000s and has remained high ever since. Meanwhile, its peers have been able to make inroads in reducing inequality.

 

Income distribution remains highly skewed. The top 20 percent of the population holds over 68 percent of income (compared to a median of 47 percent for similar emerging markets). The bottom 40 percent of the population holds 7 percent of income (compared to 16 percent for other emerging markets). Similar trends can be observed across other measures, such as the income share of the top 1 percent.

Significant disparities remain across regions. Income per capita in Gauteng—the main economic province that comprises large cities like Johannesburg and Pretoria—is almost twice the levels as that found in the mostly rural provinces like Limpopo and Eastern Cape. Being close to the economic centers increases job and income prospects.”

From a new paper on the effects of stock market dispersion:

“We study the extent to which stock market dispersion is related to unemployment and output growth for 16 countries over 20 years. Using panel vector-auto-regressions and panel dynamic regressions, we find increases in stock market dispersion across industries to induce future increases in unemployment and
future decreases in industrial production (IP) growth. Moreover, the responses of unemployment and IP growth following a positive shock to stock market dispersion are persistent and are robust to various controls, sample periods, and estimation methods. Our article provides cross-country evidence in support of the hypothesis that shifts in demand across industries negatively affect employment.”

“We present the impulse responses of the unemployment rate and the change in the interest rate to a positive one-standard-deviation shock to stock market dispersion in the upper subfigure of Figure 2. […] Overall, we find an increase in stock market dispersion to have a negative impact on the labor market in the short term, as evidenced by the positive response of the unemployment rate. Specifically, the unemployment rate increases by 0.02% following a positive one-standard-deviation shock to stock market dispersion. This increase peaks after the seventh month and gradually fades away after 15–20 months. Despite its modest magnitude at peak (0.02%), the impact of stock market dispersion on the unemployment rate is relatively long-lived, with the responses lasting beyond the 20-month horizon. Our result is consistent with previous studies that use U.S. data (e.g., Loungani, Rush, and Tave 1990, and more recently Angelidis, Sakkas, and Tessaromatis 2015) in the sense that unemployment significantly depends on the lags of stock market dispersion.”

From a new paper by IMF colleagues Kodjovi M. Eklou and Mamour Fall:

“Do discretionary spending cuts and tax increases hurt social well-being? To answer this question, we combine subjective well-being data covering over half a million of individuals across 13 European countries, with macroeconomic data on fiscal consolidations. We find that fiscal consolidations reduce individual well-being in the short run, especially when they are based on spending cuts. In addition, we show that accompanying monetary and exchange rate policies (disinflation, depreciations and the liberalization of capital flows) mitigate the well-being cost of fiscal consolidations. Finally, we investigate the well-being consequences of the two well-knowns expansionary fiscal consolidations episodes taking place in the 80s (in Denmark and Ireland). We find that even expansionary fiscal consolidations can have well-being costs. Our results may therefore shed some light on why some governments may choose to consolidate through taxes even at the cost of economic growth. Indeed, if spending cuts are to generate a large well-being loss, they can trigger an opposition and protest against a fiscal consolidation plan and hence making it politically costly.”

“Figure 4 depicts the effect of a 1 percentage point of GDP increase in the size of a fiscal consolidation conditional on being accompanied by different level of depreciation in the sample. Figure 4 shows that for higher levels of depreciation, that is for ratios of at least 6 units of local currencies per USD, fiscal consolidations do not have any statistically significant effect on well-being.”

From a new paper by IMF colleagues–Martin Cihak and Ratna Sahay:

“Global income inequality has fallen in the past two decades, in large part due to major strides in emerging market and developing economies to raise economic growth rates and reduce poverty. Financial sector policies and advances in financial technology are enabling financial inclusion, particularly in large economies such as China and India, allowing an increasing number of low-income households and small businesses to participate productively in the formal economy.

At the same time, we observe rising or high disparities in income and wealth within many countries. New data also show that economic mobility—the ability of the less well-off to improve their economic status—has stalled in recent decades. No wonder then that inequality of income, wealth, and opportunities is giving rise to populism and anti-globalization sentiments in some countries.

Can the financial sector play a role in reducing inequality? This study makes the case that it can, complementing redistributive fiscal policy in mitigating inequality. By expanding the provision of financial services to low-income households and small businesses, it can serve as a powerful lever in helping create a more inclusive society but—if not well managed—it can also amplify inequalities.

Our study examines empirical relationships between income inequality and three features of finance: depth (financial sector size relative to the economy), inclusion (access to and use of financial services by individuals and firms), and stability (absence of financial distress). We ask three questions.

First, does greater financial depth mean lower or higher inequality within countries? Building on new data sets, our analysis suggests that initially financial depth is associated with lower inequality, but only up to a point, after which inequality rises.

Second, does greater financial inclusion mean lower inequality within countries? We find that greater financial inclusion is associated with reductions in inequality. For payment services, we find evidence that benefits from inclusion are greater for those at the low end of the income distribution, reducing inequality. Both men and women benefit from financial inclusion, but inequality falls more when women have greater access. As regards access to and use of credit, the results are mixed.

Third, is there a relationship between stability and inequality within countries? Our study finds that higher inequality is associated with greater financial risks. Increases in inequality tend to be accompanied by higher growth in credit. For example, in the United States, too much credit, including to lower-income households, contributed to the 2008 crisis. Crises, in turn, lead to higher default rates, making lower-income households worse off and increasing inequality after a crisis.

Our key takeaway is that finance can help reduce inequality but is also associated with greater inequality if the financial system is not well managed. Our findings have five policy implications. First, financial inclusion policies help reduce inequality. Second, there is a case for promoting women’s financial inclusion, as inequality falls even more when policies are inclusive of women. Third, regulatory policies have a role to play in reining in excessive growth of the financial sector. Fourth, provided quality of regulation and supervision is high, financial inclusion and stability can be pursued simultaneously. Fifth, financial sector policies are a complement, not a substitute, for other policy tools—fiscal and macro-structural policies are still needed to help address inequality.”